SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 3073 papers

TitleStatusHype
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate ModelsCode0
Batch Active Learning at ScaleCode0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Regression via Linear-Sample Sparsification—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Generative Adversarial Network for Image Classification—0
Active Regression by Stratification—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
Active Learning for Human Pose Estimation—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
Active Learning for High-Dimensional Binary Features—0
Active Preference Learning with Discrete Choice Data—0
Active Learning for Graphs with Noisy Structures—0
Active Preference Learning for Large Language Models—0
Active Player Modelling—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active partitioning: inverting the paradigm of active learning—0
Active Output Selection Strategies for Multiple Learning Regression Models—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Learning for Financial Investment Reports—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Adversarial Domain Adaptation—0
Active Multi-Task Representation Learning—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Active Multi-Information Source Bayesian Quadrature—0
Active Learning for Fair and Stable Online Allocations—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Metric Learning from Relative Comparisons—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Show:102550
← PrevPage 19 of 62Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified